Inteligencia artificial y la era de las soluciones médicas inexplicables: navegando la opacidad algorítmica en la medicina actual. - Regazzoni, Carlos J. (Medicina (Buenos Aires))
Artificial intelligence (AI)-based technologies are pro foundly transforming healthcare through machine learn ing techniques and neural networks. These statistical tools revolutionize classical deterministic programming by directly learning probabilistic patterns from data. Such systems enable the resolution of clinical problems previously inaccessible due to their complexity or ambi guity, thus optimizing diagnostics, treatment, healthcare management, and biomedical research. However, this technological revolution presents an unprecedented epistemological challenge whereas traditional statis tics seek causal explanations for observed phenomena, predictive AI models prioritize predictive performance regardless of the underlying theoretical understanding. This "algorithmic opacity," resulting from models with millions of autonomously adjusted parameters, con trasts with human clinical reasoning based on analytical or heuristic methods. Medicine, historically grounded in causality and explanatory evidence, now confronts predictive tools whose internal logic is often inacces sible even to their developers. This divergence poses significant educational, professional, and ethical chal lenges, requiring physicians to acquire new conceptual competencies in advanced statistics and informatics to effectively navigate this transition. This article examines the biostatistical and conceptual foundations of this tension between prediction and explanation in AI, con trasting both approaches from the inferential process to causal evaluation. It emphasizes the urgent necessity for medicine to deeply comprehend these emerging para digms in order to critically integrate them into clinical practice and scientific research.